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A hierarchical, fuzzy inference approach to data filtration and feature prioritization in the connected manufacturing enterprise

Overview of attention for article published in Journal of Big Data, November 2018
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (61st percentile)

Mentioned by

patent
1 patent

Citations

dimensions_citation
6 Dimensions

Readers on

mendeley
16 Mendeley
Title
A hierarchical, fuzzy inference approach to data filtration and feature prioritization in the connected manufacturing enterprise
Published in
Journal of Big Data, November 2018
DOI 10.1186/s40537-018-0155-2
Authors

Phillip M. LaCasse, Wilkistar Otieno, Francisco P. Maturana

Mendeley readers

The data shown below were compiled from readership statistics for 16 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 16 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 6 38%
Student > Ph. D. Student 2 13%
Student > Bachelor 1 6%
Student > Doctoral Student 1 6%
Professor 1 6%
Other 3 19%
Unknown 2 13%
Readers by discipline Count As %
Engineering 7 44%
Computer Science 3 19%
Decision Sciences 1 6%
Chemistry 1 6%
Business, Management and Accounting 1 6%
Other 0 0%
Unknown 3 19%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 16 June 2021.
All research outputs
#6,128,229
of 18,873,384 outputs
Outputs from Journal of Big Data
#104
of 281 outputs
Outputs of similar age
#123,626
of 331,985 outputs
Outputs of similar age from Journal of Big Data
#1
of 1 outputs
Altmetric has tracked 18,873,384 research outputs across all sources so far. This one is in the 46th percentile – i.e., 46% of other outputs scored the same or lower than it.
So far Altmetric has tracked 281 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.9. This one has gotten more attention than average, scoring higher than 60% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 331,985 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 61% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them